Projects
Data Analyst
Saumya Deep's portfolio showcases a diverse range of projects that demonstrate expertise in data analysis. Each project reflects a unique blend of analytical skills and domain knowledge, providing valuable insights and solutions. It's an opportunity to explore the depth of experience and the ability to drive meaningful outcomes through data.
Project 01: Data Analytics Process
The project illustrates the Data Analytics Process through the practical example of buying a smartphone. It follows a structured approach, starting with planning the purchase, setting a budget, and determining requirements such as RAM, camera quality, and battery capacity. It then involves analyzing market trends, gathering feedback from friends, and shortlisting suitable options. Finally, the information is shared with a trusted shopkeeper to find the best match, leading to the purchase decision. This example demonstrates how data analytics can effectively guide everyday decision-making.
Project 02: Instagram User Analytics
In this project, Instagram User Analytics, MySQL Workbench was used to analyze Instagram user data to provide insights for the management team. The analysis aimed to help the product manager make informed decisions about the app's future direction. The process involved importing the dataset, creating a database, and executing queries to understand user engagement and platform algorithms. The project provided valuable insights into how Instagram generates reports and maintains user engagement, enhancing the understanding of data analysis for user-centric platforms.
Project 03: Operation and Metrics Analytics
The two case studies aim to analyze user-related metrics to uncover underlying causes of anomalies and drive strategic improvements. The first case study investigates a spike in user engagement, activity, and email interactions by examining trends in user accounts and events, utilizing MySQL Workbench for data analysis. The second case study focuses on the job_data dataset, analyzing job performance records to extract insights on system throughput, language distribution, and event trends. Both projects emphasize the importance of data-driven decision-making and the optimization of user engagement and operational efficiency through comprehensive analysis of large datasets.
Project 04: Hiring Process Analytics
The project focused on analyzing a dataset of employee hiring details from a company to extract insights aimed at improving the hiring process. Utilizing Microsoft Excel 2021, the analysis involved data cleaning, organization, and visualization techniques to uncover trends related to gender distribution, salary ranges, departmental composition, and position tiers. Key findings revealed a higher number of male employees, with the Operations Department having the most staff and the Human Resource Department the least. The project highlighted the effectiveness of data analytics in transforming raw data into actionable insights for organizational improvement.
The IMDB Movie Analytics project involves analyzing a dataset of IMDB movies to identify factors that influence their success, defined by high IMDB scores. The analysis encompasses various aspects such as genres, durations, languages, directors, and budgets to uncover trends and correlations that contribute to a movie's rating and profitability. The insights gained aim to provide actionable recommendations for stakeholders, including movie producers, directors, and investors, enabling them to make informed decisions for future projects. The analysis was conducted using Microsoft Excel 2021, employing techniques such as data cleaning, organization, analysis, visualization, and dynamic reporting.
The Bank Loan Case Study focuses on analyzing loan application data to identify patterns that influence loan defaults. As a data analyst at a finance company, the primary goal is to determine key factors that indicate a customer's likelihood of defaulting on a loan. This analysis aims to help the company mitigate financial risks and make informed decisions regarding loan approvals. Utilizing Microsoft Excel 2021, the project involves data cleaning, exploratory data analysis (EDA), and visualization to provide actionable insights that optimize the loan approval process and reduce risk.
This project aims to analyze the relationship between various car features, market categories, and pricing to assist car manufacturers in optimizing their pricing strategies and product development decisions. By examining a dataset containing 11,915 observations and 16 variables, including car make, model, engine specifications, and manufacturer's suggested retail price (MSRP), the project seeks to identify key features that significantly influence car prices and understand consumer demand trends. The insights gained will help manufacturers enhance profitability while effectively meeting market needs.
This project focuses on analyzing inbound call data from the Customer Experience (CX) team of ABC Insurance Company. The primary objective is to understand call patterns, optimize manpower allocation, and enhance customer experience by reducing the call abandon rate. Utilizing a dataset comprising 23 days of inbound call records, the analysis includes details such as agent information, call durations, queue times, and call statuses. The insights derived aim to inform staffing strategies and improve operational efficiency within the call center environment.
Project 09: Portfolio Project
The project is a comprehensive portfolio by Saumya Deep, showcasing a variety of data analytics projects that demonstrate expertise in tools such as MySQL and Advanced Excel. It includes detailed analyses on topics ranging from smartphone purchasing decisions and Instagram user analytics to operational metrics in job data and bank loan case studies. Each project outlines the objectives, methodologies, insights gained, and results achieved, reflecting a strong foundation in data cleaning, statistical analysis, and visualization techniques, ultimately aimed at driving strategic decision-making and improving operational efficiency.
